Skip to main content
SpringerLink
Log in
Book cover

International Conference on Information and Software Technologies

ICIST 2015: Information and Software Technologies pp 343–355Cite as

Weighted Classification Error Rate Estimator for the Euclidean Distance Classifier

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 538))

Abstract

Error counting estimators are among the best known and most widely used error estimation techniques. Perhaps the best known subcategory of error-counting estimators are k-fold cross-validation methods. Like most other error estimation techniques, cross-validation methods are biased. One way to correct this bias is to use a weighted average of cross-validation and resubstitution estimators. In this paper we propose a new weighted error-counting classification error rate estimator designed specially for the Euclidean distance classifier. Experiments with real world and synthetic data sets show that resubstitution, repeated 2-fold cross-validation, leave-one-out, basic bootstrap and D-method are outperformed by the proposed weighted error rate estimator (in terms of root-mean-square error).

This is a preview of subscription content, log in via an institution.

References

  1. Bache, K., Lichman, M.: UCI machine learning repository. http://archive.ics.uci.edu/ml. School of Information and Computer Science, University of California, Irvine, CA (2015)

  2. Braga-Neto, U., Dougherty, E.: Is cross-validation valid for small sample microarray classification? Bioinformatics 20(3), 374–380 (2004)

    Article  Google Scholar 

  3. Dougherty, E., Sima, C., Hua, J., Hanczar, B., Braga-Neto, U.: Performance of error estimators for classification. Curr. Bioinf. 5(1), 53–67 (2010)

    Article  Google Scholar 

  4. Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7(1), 1–26 (1979)

    Article  MathSciNet  Google Scholar 

  5. Efron, B.: Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78(382), 316–331 (1983)

    Article  MathSciNet  Google Scholar 

  6. Efron, B., Tibshirani, R.: Improvements on cross-validation: the 632+ bootstrap method. J. Am. Stat. Assoc. 92(438), 548–560 (1997)

    MathSciNet  MATH  Google Scholar 

  7. Fisher, R.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)

    Article  Google Scholar 

  8. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1137–1143 (1995)

    Google Scholar 

  9. Krzanowski, W.J., Hand, D.J.: Assessing error rate estimators: the leaving-one-out reconsidered. Aust. J. Stat. 39(1), 35–46 (1997)

    Article  Google Scholar 

  10. Lachenbruch, P., Mickey, R.: Estimation of error rates in discriminant analysis. Technometrics 10(1), 1–11 (1968)

    Article  MathSciNet  Google Scholar 

  11. McLachlan, G.J.: A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. Pattern Recogn. 9, 147–149 (1977)

    Article  Google Scholar 

  12. Raudys, S.: Statistical and Neural Classifiers, An Integrated Approach to Design. Springer, London (2001)

    Book  Google Scholar 

  13. Rodriguez, J.D., Perez, A., Lozano, J.A.: A general framework for the statistical analysis of the sources of variance for classification error estimators. Pattern Recogn. 46, 855–864 (2013)

    Article  Google Scholar 

  14. Sima, C., Dougherty, E.: Optimal convex error estimators for classification. Pattern Recogn. 39(6), 1763–1780 (2006)

    Article  Google Scholar 

  15. Smith, C.: Some examples of discrimination. Ann. Eugenics 18, 272–282 (1947)

    MathSciNet  Google Scholar 

  16. Toussaint, G., Sharpe, P.: An efficient method for estimating the probability of misclassification applied to a problem in medical diagnosis. Comput. Biol. Med. 4, 269–278 (1975)

    Article  Google Scholar 

  17. Zollanvari, A., Braga-Neto, U., Dougherty, E.: Analytic study of performance of error estimators for linear discriminant analysis. IEEE Trans. Signal Process. 59(9), 4238–4255 (2011)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of System Analysis, Vytautas Magnus University, Vileikos St. 8, 44404, Kaunas, Lithuania

    Mindaugas Gvardinskas

Authors
  1. Mindaugas Gvardinskas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mindaugas Gvardinskas .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damasevicius

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Gvardinskas, M. (2015). Weighted Classification Error Rate Estimator for the Euclidean Distance Classifier. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2015. Communications in Computer and Information Science, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-24770-0_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24770-0_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24769-4

  • Online ISBN: 978-3-319-24770-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation